trajrl 0.3.1

Official skill collection for TrajectoryRL (Bittensor SN11) — discover and run subnet skills.

trajrl

Official Python package for TrajectoryRL.

trajrl publishes skills powered by the subnet — ready-to-use capabilities for AI agents, miners, and developers. Each skill is a self-contained SKILL.md that agents can discover and follow directly.

Designed for AI agents (Claude Code, Cursor, Codex) and humans alike — outputs JSON when piped, Rich tables when interactive.

Install

pip install trajrl

Skills

Skills are the core of this package. Each skill lives in skills/<name>/SKILL.md and provides everything an agent needs: context, CLI commands, and data concepts.

subnet-analyze

Deep analysis of TrajectoryRL subnet data — validators, miners, scores, weight distribution, scenarios, eval logs.

What an agent can do with this skill:

# Full validator analysis — scores, weights, scenarios, leaderboard
trajrl subnet analyze 5FFApaS7...

# Drill into top miners
trajrl subnet analyze 5FFApaS7... --deep

# Network overview
trajrl subnet status

# JSON output for piping (automatic when piped)
trajrl subnet status | jq '.validators.validators[].hotkey'

See skills/subnet-analyze/SKILL.md for full usage reference.

API Reference

Subnet data comes from the TrajectoryRL Public API — read-only, no authentication required.